Overview

Dataset statistics

Number of variables19
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.3 KiB
Average record size in memory152.7 B

Variable types

Numeric18
Categorical1

Alerts

Dacoity is highly correlated with Robbery and 15 other fieldsHigh correlation
Robbery is highly correlated with Dacoity and 15 other fieldsHigh correlation
Murder is highly correlated with Dacoity and 15 other fieldsHigh correlation
Speedy Trial is highly correlated with Dacoity and 15 other fieldsHigh correlation
Riot is highly correlated with Dacoity and 15 other fieldsHigh correlation
Woman & Child Repression is highly correlated with Dacoity and 15 other fieldsHigh correlation
Kidnapping is highly correlated with Dacoity and 15 other fieldsHigh correlation
Police Assault is highly correlated with Dacoity and 15 other fieldsHigh correlation
Burglary is highly correlated with Dacoity and 15 other fieldsHigh correlation
Theft is highly correlated with Dacoity and 15 other fieldsHigh correlation
Other Cases is highly correlated with Dacoity and 15 other fieldsHigh correlation
Arms Act - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Explosive - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Narcotics - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Smuggling - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Total - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Total Cases is highly correlated with Dacoity and 15 other fieldsHigh correlation
Dacoity is highly correlated with Robbery and 15 other fieldsHigh correlation
Robbery is highly correlated with Dacoity and 15 other fieldsHigh correlation
Murder is highly correlated with Dacoity and 15 other fieldsHigh correlation
Speedy Trial is highly correlated with Dacoity and 15 other fieldsHigh correlation
Riot is highly correlated with Dacoity and 15 other fieldsHigh correlation
Woman & Child Repression is highly correlated with Dacoity and 15 other fieldsHigh correlation
Kidnapping is highly correlated with Dacoity and 15 other fieldsHigh correlation
Police Assault is highly correlated with Dacoity and 15 other fieldsHigh correlation
Burglary is highly correlated with Dacoity and 15 other fieldsHigh correlation
Theft is highly correlated with Dacoity and 15 other fieldsHigh correlation
Other Cases is highly correlated with Dacoity and 15 other fieldsHigh correlation
Arms Act - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Explosive - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Narcotics - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Smuggling - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Total - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Total Cases is highly correlated with Dacoity and 15 other fieldsHigh correlation
Dacoity is highly correlated with Robbery and 15 other fieldsHigh correlation
Robbery is highly correlated with Dacoity and 15 other fieldsHigh correlation
Murder is highly correlated with Dacoity and 14 other fieldsHigh correlation
Speedy Trial is highly correlated with Dacoity and 15 other fieldsHigh correlation
Riot is highly correlated with Dacoity and 6 other fieldsHigh correlation
Woman & Child Repression is highly correlated with Dacoity and 14 other fieldsHigh correlation
Kidnapping is highly correlated with Dacoity and 15 other fieldsHigh correlation
Police Assault is highly correlated with Dacoity and 15 other fieldsHigh correlation
Burglary is highly correlated with Dacoity and 15 other fieldsHigh correlation
Theft is highly correlated with Dacoity and 15 other fieldsHigh correlation
Other Cases is highly correlated with Dacoity and 14 other fieldsHigh correlation
Arms Act - RC is highly correlated with Dacoity and 14 other fieldsHigh correlation
Explosive - RC is highly correlated with Dacoity and 14 other fieldsHigh correlation
Narcotics - RC is highly correlated with Dacoity and 14 other fieldsHigh correlation
Smuggling - RC is highly correlated with Dacoity and 14 other fieldsHigh correlation
Total - RC is highly correlated with Dacoity and 14 other fieldsHigh correlation
Total Cases is highly correlated with Dacoity and 14 other fieldsHigh correlation
Unit Name is highly correlated with Dacoity and 14 other fieldsHigh correlation
Dacoity is highly correlated with Unit Name and 16 other fieldsHigh correlation
Robbery is highly correlated with Unit Name and 16 other fieldsHigh correlation
Murder is highly correlated with Unit Name and 16 other fieldsHigh correlation
Speedy Trial is highly correlated with Unit Name and 16 other fieldsHigh correlation
Riot is highly correlated with Dacoity and 15 other fieldsHigh correlation
Woman & Child Repression is highly correlated with Unit Name and 16 other fieldsHigh correlation
Kidnapping is highly correlated with Unit Name and 16 other fieldsHigh correlation
Police Assault is highly correlated with Unit Name and 16 other fieldsHigh correlation
Burglary is highly correlated with Unit Name and 16 other fieldsHigh correlation
Theft is highly correlated with Unit Name and 16 other fieldsHigh correlation
Other Cases is highly correlated with Unit Name and 16 other fieldsHigh correlation
Arms Act - RC is highly correlated with Unit Name and 16 other fieldsHigh correlation
Explosive - RC is highly correlated with Dacoity and 15 other fieldsHigh correlation
Narcotics - RC is highly correlated with Unit Name and 16 other fieldsHigh correlation
Smuggling - RC is highly correlated with Unit Name and 16 other fieldsHigh correlation
Total - RC is highly correlated with Unit Name and 16 other fieldsHigh correlation
Total Cases is highly correlated with Unit Name and 16 other fieldsHigh correlation
Unit Name is uniformly distributed Uniform
Dacoity has 47 (24.7%) zeros Zeros
Robbery has 37 (19.5%) zeros Zeros
Murder has 35 (18.4%) zeros Zeros
Speedy Trial has 39 (20.5%) zeros Zeros
Riot has 102 (53.7%) zeros Zeros
Woman & Child Repression has 35 (18.4%) zeros Zeros
Kidnapping has 42 (22.1%) zeros Zeros
Police Assault has 41 (21.6%) zeros Zeros
Burglary has 39 (20.5%) zeros Zeros
Theft has 34 (17.9%) zeros Zeros
Other Cases has 34 (17.9%) zeros Zeros
Arms Act - RC has 37 (19.5%) zeros Zeros
Explosive - RC has 60 (31.6%) zeros Zeros
Narcotics - RC has 34 (17.9%) zeros Zeros
Smuggling - RC has 39 (20.5%) zeros Zeros
Total - RC has 34 (17.9%) zeros Zeros
Total Cases has 34 (17.9%) zeros Zeros

Reproduction

Analysis started2022-01-09 23:48:18.286119
Analysis finished2022-01-09 23:49:15.516810
Duration57.23 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

Distinct10
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5
Minimum2010
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:15.598407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2014.5
Q32017
95-th percentile2019
Maximum2019
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.879869927
Coefficient of variation (CV)0.001429570577
Kurtosis-1.224824419
Mean2014.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum382755
Variance8.293650794
MonotonicityIncreasing
2022-01-10T05:49:15.813831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
201019
10.0%
201119
10.0%
201219
10.0%
201319
10.0%
201419
10.0%
201519
10.0%
201619
10.0%
201719
10.0%
201819
10.0%
201919
10.0%
ValueCountFrequency (%)
201019
10.0%
201119
10.0%
201219
10.0%
201319
10.0%
201419
10.0%
201519
10.0%
201619
10.0%
201719
10.0%
201819
10.0%
201919
10.0%
ValueCountFrequency (%)
201919
10.0%
201819
10.0%
201719
10.0%
201619
10.0%
201519
10.0%
201419
10.0%
201319
10.0%
201219
10.0%
201119
10.0%
201019
10.0%

Unit Name
Categorical

HIGH CORRELATION
UNIFORM

Distinct19
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
DMP
 
10
Khulna Range
 
10
ATU
 
10
RPMP
 
10
GMP
 
10
Other values (14)
140 

Length

Max length16
Median length5
Mean length8.052631579
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDMP
2nd rowCMP
3rd rowKMP
4th rowRMP
5th rowBMP

Common Values

ValueCountFrequency (%)
DMP10
 
5.3%
Khulna Range10
 
5.3%
ATU10
 
5.3%
RPMP10
 
5.3%
GMP10
 
5.3%
Railway Range10
 
5.3%
Rangpur Range10
 
5.3%
Rajshahi Range10
 
5.3%
Barisal Range10
 
5.3%
Sylhet Range10
 
5.3%
Other values (9)90
47.4%

Length

2022-01-10T05:49:15.940492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
range90
32.1%
dmp10
 
3.6%
cmp10
 
3.6%
kmp10
 
3.6%
rmp10
 
3.6%
bmp10
 
3.6%
smp10
 
3.6%
dhaka10
 
3.6%
mymensingh10
 
3.6%
chittagong10
 
3.6%
Other values (10)100
35.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Dacoity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct76
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4
Minimum0
Maximum656
Zeros47
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:16.105097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q342.5
95-th percentile175
Maximum656
Range656
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation116.1028462
Coefficient of variation (CV)2.350260045
Kurtosis16.50214315
Mean49.4
Median Absolute Deviation (MAD)8
Skewness3.981813951
Sum9386
Variance13479.8709
MonotonicityNot monotonic
2022-01-10T05:49:16.269614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047
24.7%
211
 
5.8%
19
 
4.7%
37
 
3.7%
46
 
3.2%
56
 
3.2%
475
 
2.6%
65
 
2.6%
85
 
2.6%
205
 
2.6%
Other values (66)84
44.2%
ValueCountFrequency (%)
047
24.7%
19
 
4.7%
211
 
5.8%
37
 
3.7%
46
 
3.2%
56
 
3.2%
65
 
2.6%
73
 
1.6%
85
 
2.6%
92
 
1.1%
ValueCountFrequency (%)
6561
0.5%
6511
0.5%
6501
0.5%
6131
0.5%
5931
0.5%
4921
0.5%
4081
0.5%
3361
0.5%
2621
0.5%
1841
0.5%

Robbery
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.42105263
Minimum0
Maximum1155
Zeros37
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:16.434172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median24
Q370.25
95-th percentile280.95
Maximum1155
Range1155
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation196.803938
Coefficient of variation (CV)2.277268467
Kurtosis16.35737471
Mean86.42105263
Median Absolute Deviation (MAD)24
Skewness4.009692003
Sum16420
Variance38731.79003
MonotonicityNot monotonic
2022-01-10T05:49:16.589799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
19.5%
86
 
3.2%
116
 
3.2%
295
 
2.6%
184
 
2.1%
44
 
2.1%
14
 
2.1%
104
 
2.1%
663
 
1.6%
93
 
1.6%
Other values (85)114
60.0%
ValueCountFrequency (%)
037
19.5%
14
 
2.1%
22
 
1.1%
33
 
1.6%
44
 
2.1%
52
 
1.1%
61
 
0.5%
72
 
1.1%
86
 
3.2%
93
 
1.6%
ValueCountFrequency (%)
11551
0.5%
10691
0.5%
10591
0.5%
10211
0.5%
9641
0.5%
9331
0.5%
7221
0.5%
6571
0.5%
5621
0.5%
2941
0.5%

Murder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct119
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.4526316
Minimum0
Maximum4514
Zeros35
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:16.737404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.25
median41
Q3343.25
95-th percentile1353.6
Maximum4514
Range4514
Interquartile range (IQR)328

Descriptive statistics

Standard deviation860.004193
Coefficient of variation (CV)2.248655447
Kurtosis13.02556851
Mean382.4526316
Median Absolute Deviation (MAD)41
Skewness3.648940838
Sum72666
Variance739607.212
MonotonicityNot monotonic
2022-01-10T05:49:16.911897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035
 
18.4%
335
 
2.6%
375
 
2.6%
154
 
2.1%
194
 
2.1%
204
 
2.1%
293
 
1.6%
163
 
1.6%
233
 
1.6%
223
 
1.6%
Other values (109)121
63.7%
ValueCountFrequency (%)
035
18.4%
12
 
1.1%
21
 
0.5%
32
 
1.1%
41
 
0.5%
51
 
0.5%
81
 
0.5%
91
 
0.5%
154
 
2.1%
163
 
1.6%
ValueCountFrequency (%)
45141
0.5%
43931
0.5%
41141
0.5%
40371
0.5%
39881
0.5%
39661
0.5%
38301
0.5%
35911
0.5%
35491
0.5%
13951
0.5%

Speedy Trial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct103
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.8315789
Minimum0
Maximum1907
Zeros39
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:17.086430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median28.5
Q3123.25
95-th percentile522.5
Maximum1907
Range1907
Interquartile range (IQR)121.25

Descriptive statistics

Standard deviation335.2353087
Coefficient of variation (CV)2.330749
Kurtosis16.42396921
Mean143.8315789
Median Absolute Deviation (MAD)28.5
Skewness3.984212631
Sum27328
Variance112382.7122
MonotonicityNot monotonic
2022-01-10T05:49:17.244018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
20.5%
37
 
3.7%
196
 
3.2%
25
 
2.6%
15
 
2.6%
54
 
2.1%
44
 
2.1%
523
 
1.6%
1152
 
1.1%
1052
 
1.1%
Other values (93)113
59.5%
ValueCountFrequency (%)
039
20.5%
15
 
2.6%
25
 
2.6%
37
 
3.7%
44
 
2.1%
54
 
2.1%
62
 
1.1%
71
 
0.5%
82
 
1.1%
92
 
1.1%
ValueCountFrequency (%)
19071
0.5%
18961
0.5%
18631
0.5%
17161
0.5%
16661
0.5%
15491
0.5%
10521
0.5%
10451
0.5%
9221
0.5%
5631
0.5%

Riot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.210526316
Minimum0
Maximum172
Zeros102
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:17.388619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile30.65
Maximum172
Range172
Interquartile range (IQR)7

Descriptive statistics

Standard deviation21.68821626
Coefficient of variation (CV)2.641513519
Kurtosis26.21544752
Mean8.210526316
Median Absolute Deviation (MAD)0
Skewness4.740402406
Sum1560
Variance470.3787246
MonotonicityNot monotonic
2022-01-10T05:49:17.513287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0102
53.7%
117
 
8.9%
38
 
4.2%
176
 
3.2%
76
 
3.2%
155
 
2.6%
25
 
2.6%
113
 
1.6%
93
 
1.6%
53
 
1.6%
Other values (27)32
 
16.8%
ValueCountFrequency (%)
0102
53.7%
117
 
8.9%
25
 
2.6%
38
 
4.2%
42
 
1.1%
53
 
1.6%
62
 
1.1%
76
 
3.2%
81
 
0.5%
93
 
1.6%
ValueCountFrequency (%)
1721
0.5%
1301
0.5%
1091
0.5%
941
0.5%
931
0.5%
791
0.5%
561
0.5%
531
0.5%
371
0.5%
321
0.5%

Woman & Child Repression
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct142
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1843.168421
Minimum0
Maximum21389
Zeros35
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:17.659896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median211
Q31863.25
95-th percentile5082.15
Maximum21389
Range21389
Interquartile range (IQR)1847.25

Descriptive statistics

Standard deviation4136.958354
Coefficient of variation (CV)2.244482006
Kurtosis13.75360953
Mean1843.168421
Median Absolute Deviation (MAD)211
Skewness3.735964866
Sum350202
Variance17114424.43
MonotonicityNot monotonic
2022-01-10T05:49:17.842453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035
 
18.4%
52
 
1.1%
1472
 
1.1%
222
 
1.1%
1582
 
1.1%
15382
 
1.1%
1202
 
1.1%
82
 
1.1%
42
 
1.1%
8482
 
1.1%
Other values (132)137
72.1%
ValueCountFrequency (%)
035
18.4%
11
 
0.5%
31
 
0.5%
42
 
1.1%
52
 
1.1%
61
 
0.5%
82
 
1.1%
111
 
0.5%
121
 
0.5%
141
 
0.5%
ValueCountFrequency (%)
213891
0.5%
212911
0.5%
212101
0.5%
209471
0.5%
196011
0.5%
184461
0.5%
177521
0.5%
170731
0.5%
162531
0.5%
51151
0.5%

Kidnapping
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct84
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.09473684
Minimum0
Maximum920
Zeros42
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:18.013950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median16
Q364.5
95-th percentile203.55
Maximum920
Range920
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation162.5360895
Coefficient of variation (CV)2.286190184
Kurtosis16.08359822
Mean71.09473684
Median Absolute Deviation (MAD)16
Skewness3.975249738
Sum13508
Variance26417.9804
MonotonicityNot monotonic
2022-01-10T05:49:18.176515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042
 
22.1%
16
 
3.2%
115
 
2.6%
65
 
2.6%
35
 
2.6%
85
 
2.6%
25
 
2.6%
165
 
2.6%
44
 
2.1%
273
 
1.6%
Other values (74)105
55.3%
ValueCountFrequency (%)
042
22.1%
16
 
3.2%
25
 
2.6%
35
 
2.6%
44
 
2.1%
53
 
1.6%
65
 
2.6%
73
 
1.6%
85
 
2.6%
93
 
1.6%
ValueCountFrequency (%)
9201
0.5%
8791
0.5%
8701
0.5%
8501
0.5%
8051
0.5%
7921
0.5%
6391
0.5%
5091
0.5%
4441
0.5%
2041
0.5%

Police Assault
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.78947368
Minimum0
Maximum1257
Zeros41
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:18.454815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q346
95-th percentile291
Maximum1257
Range1257
Interquartile range (IQR)44

Descriptive statistics

Standard deviation155.8036272
Coefficient of variation (CV)2.368215134
Kurtosis23.97803298
Mean65.78947368
Median Absolute Deviation (MAD)16
Skewness4.487585559
Sum12500
Variance24274.77026
MonotonicityNot monotonic
2022-01-10T05:49:18.592453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
 
21.6%
135
 
2.6%
15
 
2.6%
125
 
2.6%
85
 
2.6%
25
 
2.6%
35
 
2.6%
164
 
2.1%
204
 
2.1%
994
 
2.1%
Other values (71)107
56.3%
ValueCountFrequency (%)
041
21.6%
15
 
2.6%
25
 
2.6%
35
 
2.6%
44
 
2.1%
53
 
1.6%
64
 
2.1%
85
 
2.6%
92
 
1.1%
102
 
1.1%
ValueCountFrequency (%)
12571
0.5%
8111
0.5%
7021
0.5%
6591
0.5%
6341
0.5%
5811
0.5%
5431
0.5%
5211
0.5%
4731
0.5%
3361
0.5%

Burglary
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct116
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.7368421
Minimum0
Maximum3134
Zeros39
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:18.765936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median56
Q3189
95-th percentile669.8
Maximum3134
Range3134
Interquartile range (IQR)185.75

Descriptive statistics

Standard deviation568.0452644
Coefficient of variation (CV)2.256504291
Kurtosis14.53991224
Mean251.7368421
Median Absolute Deviation (MAD)56
Skewness3.808888076
Sum47830
Variance322675.4224
MonotonicityNot monotonic
2022-01-10T05:49:18.941467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
20.5%
25
 
2.6%
323
 
1.6%
13
 
1.6%
443
 
1.6%
403
 
1.6%
73
 
1.6%
333
 
1.6%
253
 
1.6%
743
 
1.6%
Other values (106)122
64.2%
ValueCountFrequency (%)
039
20.5%
13
 
1.6%
25
 
2.6%
31
 
0.5%
42
 
1.1%
52
 
1.1%
62
 
1.1%
73
 
1.6%
81
 
0.5%
161
 
0.5%
ValueCountFrequency (%)
31341
0.5%
31011
0.5%
29271
0.5%
28091
0.5%
27621
0.5%
24951
0.5%
22131
0.5%
21631
0.5%
21371
0.5%
6861
0.5%

Theft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct140
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.5368421
Minimum0
Maximum8873
Zeros34
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:19.114044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136.5
median139
Q3521.25
95-th percentile2220.2
Maximum8873
Range8873
Interquartile range (IQR)484.75

Descriptive statistics

Standard deviation1584.56322
Coefficient of variation (CV)2.268403218
Kurtosis14.87911297
Mean698.5368421
Median Absolute Deviation (MAD)139
Skewness3.840639794
Sum132722
Variance2510840.599
MonotonicityNot monotonic
2022-01-10T05:49:19.268630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
17.9%
503
 
1.6%
63
 
1.6%
1013
 
1.6%
4772
 
1.1%
892
 
1.1%
1362
 
1.1%
462
 
1.1%
5132
 
1.1%
782
 
1.1%
Other values (130)135
71.1%
ValueCountFrequency (%)
034
17.9%
21
 
0.5%
41
 
0.5%
51
 
0.5%
63
 
1.6%
81
 
0.5%
151
 
0.5%
171
 
0.5%
231
 
0.5%
241
 
0.5%
ValueCountFrequency (%)
88731
0.5%
85981
0.5%
85291
0.5%
78821
0.5%
76601
0.5%
68211
0.5%
61101
0.5%
58331
0.5%
55611
0.5%
22401
0.5%

Other Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct156
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8080.094737
Minimum0
Maximum96112
Zeros34
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:19.411256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1149
median861.5
Q38272.75
95-th percentile22216.6
Maximum96112
Range96112
Interquartile range (IQR)8123.75

Descriptive statistics

Standard deviation18080.14797
Coefficient of variation (CV)2.237615839
Kurtosis13.95087721
Mean8080.094737
Median Absolute Deviation (MAD)861.5
Skewness3.765501324
Sum1535218
Variance326891750.5
MonotonicityNot monotonic
2022-01-10T05:49:19.554861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
17.9%
7282
 
1.1%
72281
 
0.5%
3881
 
0.5%
85231
 
0.5%
1311
 
0.5%
777471
 
0.5%
53151
 
0.5%
15471
 
0.5%
4431
 
0.5%
Other values (146)146
76.8%
ValueCountFrequency (%)
034
17.9%
91
 
0.5%
161
 
0.5%
331
 
0.5%
491
 
0.5%
521
 
0.5%
551
 
0.5%
651
 
0.5%
901
 
0.5%
1001
 
0.5%
ValueCountFrequency (%)
961121
0.5%
939301
0.5%
904001
0.5%
883551
0.5%
871391
0.5%
841171
0.5%
777471
0.5%
746451
0.5%
697361
0.5%
224291
0.5%

Arms Act - RC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct104
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.6526316
Minimum0
Maximum2515
Zeros37
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:19.699481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median25.5
Q3206.25
95-th percentile659.1
Maximum2515
Range2515
Interquartile range (IQR)203.25

Descriptive statistics

Standard deviation415.6922006
Coefficient of variation (CV)2.301058097
Kurtosis15.73876612
Mean180.6526316
Median Absolute Deviation (MAD)25.5
Skewness3.88312043
Sum34324
Variance172800.0057
MonotonicityNot monotonic
2022-01-10T05:49:19.860051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
19.5%
36
 
3.2%
175
 
2.6%
165
 
2.6%
54
 
2.1%
114
 
2.1%
184
 
2.1%
14
 
2.1%
133
 
1.6%
193
 
1.6%
Other values (94)115
60.5%
ValueCountFrequency (%)
037
19.5%
14
 
2.1%
23
 
1.6%
36
 
3.2%
42
 
1.1%
54
 
2.1%
61
 
0.5%
82
 
1.1%
101
 
0.5%
114
 
2.1%
ValueCountFrequency (%)
25151
0.5%
22911
0.5%
22081
0.5%
20791
0.5%
20231
0.5%
15751
0.5%
15171
0.5%
15111
0.5%
12691
0.5%
7231
0.5%

Explosive - RC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct71
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.63157895
Minimum0
Maximum1310
Zeros60
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:20.027608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q339.25
95-th percentile232.3
Maximum1310
Range1310
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation149.4862137
Coefficient of variation (CV)2.736260174
Kurtosis36.61876281
Mean54.63157895
Median Absolute Deviation (MAD)5.5
Skewness5.500918595
Sum10380
Variance22346.1281
MonotonicityNot monotonic
2022-01-10T05:49:20.166237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060
31.6%
39
 
4.7%
19
 
4.7%
27
 
3.7%
47
 
3.7%
104
 
2.1%
84
 
2.1%
154
 
2.1%
114
 
2.1%
73
 
1.6%
Other values (61)79
41.6%
ValueCountFrequency (%)
060
31.6%
19
 
4.7%
27
 
3.7%
39
 
4.7%
47
 
3.7%
53
 
1.6%
63
 
1.6%
73
 
1.6%
84
 
2.1%
92
 
1.1%
ValueCountFrequency (%)
13101
0.5%
10071
0.5%
7251
0.5%
5201
0.5%
4871
0.5%
3871
0.5%
3621
0.5%
3541
0.5%
2891
0.5%
2531
0.5%

Narcotics - RC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct155
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5338.031579
Minimum0
Maximum112549
Zeros34
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:20.303870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1188
median970
Q34450.5
95-th percentile21315.8
Maximum112549
Range112549
Interquartile range (IQR)4262.5

Descriptive statistics

Standard deviation13451.2835
Coefficient of variation (CV)2.519895826
Kurtosis34.91635257
Mean5338.031579
Median Absolute Deviation (MAD)970
Skewness5.399773913
Sum1014226
Variance180937027.7
MonotonicityNot monotonic
2022-01-10T05:49:20.435470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
17.9%
3952
 
1.1%
2642
 
1.1%
105351
 
0.5%
20211
 
0.5%
44251
 
0.5%
5181
 
0.5%
622081
 
0.5%
136381
 
0.5%
42601
 
0.5%
Other values (145)145
76.3%
ValueCountFrequency (%)
034
17.9%
441
 
0.5%
551
 
0.5%
681
 
0.5%
911
 
0.5%
1301
 
0.5%
1391
 
0.5%
1441
 
0.5%
1541
 
0.5%
1551
 
0.5%
ValueCountFrequency (%)
1125491
0.5%
989841
0.5%
622081
0.5%
476661
0.5%
425011
0.5%
372641
0.5%
358321
0.5%
316961
0.5%
293441
0.5%
226821
0.5%

Smuggling - RC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct121
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560
Minimum0
Maximum6788
Zeros39
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:20.576140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median84.5
Q3245.25
95-th percentile2404.15
Maximum6788
Range6788
Interquartile range (IQR)242

Descriptive statistics

Standard deviation1310.060591
Coefficient of variation (CV)2.339393912
Kurtosis11.95054994
Mean560
Median Absolute Deviation (MAD)84.5
Skewness3.452754174
Sum106400
Variance1716258.751
MonotonicityNot monotonic
2022-01-10T05:49:20.901225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
20.5%
25
 
2.6%
44
 
2.1%
93
 
1.6%
13
 
1.6%
312
 
1.1%
1342
 
1.1%
192
 
1.1%
1152
 
1.1%
5312
 
1.1%
Other values (111)126
66.3%
ValueCountFrequency (%)
039
20.5%
13
 
1.6%
25
 
2.6%
31
 
0.5%
44
 
2.1%
52
 
1.1%
61
 
0.5%
71
 
0.5%
82
 
1.1%
93
 
1.6%
ValueCountFrequency (%)
67881
0.5%
65781
0.5%
64371
0.5%
63631
0.5%
61791
0.5%
57141
0.5%
55991
0.5%
46801
0.5%
45011
0.5%
25091
0.5%

Total - RC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct154
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6133.315789
Minimum0
Maximum120875
Zeros34
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:21.065822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1279.5
median1099.5
Q35589.25
95-th percentile21835.9
Maximum120875
Range120875
Interquartile range (IQR)5309.75

Descriptive statistics

Standard deviation14974.72684
Coefficient of variation (CV)2.441538534
Kurtosis30.81056578
Mean6133.315789
Median Absolute Deviation (MAD)1099.5
Skewness5.097729698
Sum1165330
Variance224242444
MonotonicityNot monotonic
2022-01-10T05:49:21.231380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
17.9%
2892
 
1.1%
4882
 
1.1%
5722
 
1.1%
112791
 
0.5%
17671
 
0.5%
116851
 
0.5%
57581
 
0.5%
6341
 
0.5%
696661
 
0.5%
Other values (144)144
75.8%
ValueCountFrequency (%)
034
17.9%
461
 
0.5%
671
 
0.5%
681
 
0.5%
921
 
0.5%
1351
 
0.5%
1641
 
0.5%
1881
 
0.5%
1891
 
0.5%
2021
 
0.5%
ValueCountFrequency (%)
1208751
0.5%
1071531
0.5%
696661
0.5%
566491
0.5%
518321
0.5%
456421
0.5%
447931
0.5%
388861
0.5%
375351
0.5%
231941
0.5%

Total Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct157
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17814.05263
Minimum0
Maximum221419
Zeros34
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-01-10T05:49:21.385973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1831.75
median2814.5
Q319893.5
95-th percentile44318.2
Maximum221419
Range221419
Interquartile range (IQR)19061.75

Descriptive statistics

Standard deviation39531.00148
Coefficient of variation (CV)2.219090866
Kurtosis14.30990129
Mean17814.05263
Median Absolute Deviation (MAD)2814.5
Skewness3.812193411
Sum3384670
Variance1562700078
MonotonicityNot monotonic
2022-01-10T05:49:21.522607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
17.9%
235191
 
0.5%
30171
 
0.5%
231691
 
0.5%
173231
 
0.5%
10151
 
0.5%
1811681
 
0.5%
236301
 
0.5%
70441
 
0.5%
26121
 
0.5%
Other values (147)147
77.4%
ValueCountFrequency (%)
034
17.9%
841
 
0.5%
1211
 
0.5%
1431
 
0.5%
1661
 
0.5%
2441
 
0.5%
3171
 
0.5%
3521
 
0.5%
4011
 
0.5%
6391
 
0.5%
ValueCountFrequency (%)
2214191
0.5%
2135291
0.5%
1837291
0.5%
1834071
0.5%
1811681
0.5%
1798351
0.5%
1791991
0.5%
1696671
0.5%
1628981
0.5%
445361
0.5%

Interactions

2022-01-10T05:49:12.756337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:39.437520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:42.031527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.769878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.614120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:47.433780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:49.390576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:51.309337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:53.140386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.862779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.854454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.805574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.544961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:02.439170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:04.306164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:06.316725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:08.309583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:10.567194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.849149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:40.326088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-10T05:48:48.747266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:50.705945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:52.570910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.302279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.187237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.209811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:59.948545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:01.825804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:03.736726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:05.513200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:07.711172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:09.842178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:11.938569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:14.071819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:41.472023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.278193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.133367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:46.813439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:48.858968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:50.801689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:52.658675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.391084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.298939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.303523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.040272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:01.920546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:03.825449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:05.613904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:07.812864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:09.944899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.046237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:14.204561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:41.561795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.373937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.232113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:46.911182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:48.959701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:50.900426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:52.753449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.486831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.399669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.410239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.136059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:02.028215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:03.922189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:05.718623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:07.909649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:10.051618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.169905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:14.315264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:41.649548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.466708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.326849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:47.123650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:49.061471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:51.003144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:52.851160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.582575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.512367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.513000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.231759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:02.137922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:04.016935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:05.828369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:08.002357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:10.155327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.280609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:14.435943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:41.849015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.571408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.427620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:47.224412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:49.169139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:51.118794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:52.947901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.683260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.636037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.609116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.343461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:02.253613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:04.118663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:05.953994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:08.108075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:10.284950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.522961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:14.541659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:41.941786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:43.679120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:45.525318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:47.334046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:49.271910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:51.218527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:53.049636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:54.777009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:56.752725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:48:58.709829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:00.446233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:02.349357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:04.215404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:06.086639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:08.221771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:10.428565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-10T05:49:12.644636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-10T05:49:21.672354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-10T05:49:21.910616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-10T05:49:22.134019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-10T05:49:22.395277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-10T05:49:14.767056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-10T05:49:15.104217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

YearUnit NameDacoityRobberyMurderSpeedy TrialRiotWoman & Child RepressionKidnappingPolice AssaultBurglaryTheftOther CasesArms Act - RCExplosive - RCNarcotics - RCSmuggling - RCTotal - RCTotal Cases
02010DMP47220245363313701391555551915722851882105351441127923519
12010CMP1610894317455373112331418315108669910164063
22010KMP39292501531146591551192792138261767
32010RMP4202191515791253106578343322485871571
42010BMP812192101126824835571701551172891139
52010SMP123333341104141233154866140154201881484
62010Dhaka Range16219911533627427217171643147719966309304459993579134274
72010Mymensingh Range00000000000000000
82010Chittagong Range1531226392453229151118742999812985235204730612559724313
92010Sylhet Range854324575178484119186524526630490517211118460

Last rows

YearUnit NameDacoityRobberyMurderSpeedy TrialRiotWoman & Child RepressionKidnappingPolice AssaultBurglaryTheftOther CasesArms Act - RCExplosive - RCNarcotics - RCSmuggling - RCTotal - RCTotal Cases
1802019Sylhet Range45204067187334121313921164725
1812019Khulna Range4437009922736544133814208501585
1822019Barisal Range23162086321623378103630364895
1832019Rajshahi Range58296099825404692515116613413402011
1842019Rangpur Range1233001160083862540551946491472
1852019Railway Range012000000590055126784
1862019GMP2331022122865301302135244
1872019RPMP00100121006330068068121
1882019ATU00000000000000000
1892019Total3268351481113946691744945428174309069361963417484